Wait Times for Scheduling Appointments for Prevention of Macrovascular and Microvascular Complications of Diabetes: Cross-Sectional Descriptive Study

BACKGROUND

Diabetes is a chronic disease that requires lifelong management and care, affecting around 422 million people worldwide and roughly 37 million in the United States. Patients newly diagnosed with diabetes must work with health care providers to formulate a management plan, including lifestyle modifications and regular office visits, to improve metabolic control, prevent or delay complications, optimize quality of life, and promote well-being.

OBJECTIVE

Our aim is to investigate one component of system-wide access to timely health care for people with diabetes in New York City (NYC), namely the length of time for someone with newly diagnosed diabetes to obtain an appointment with 3 diabetes care specialists: a cardiologist, an endocrinologist, and an ophthalmologist, respectively.

METHODS

We contacted the offices of 3 different kinds of specialists: cardiologists, endocrinologists, and ophthalmologists, by telephone, for this descriptive cross-sectional study, to determine the number of days required to schedule an appointment for a new patient with diabetes. The sampling frame included all specialists affiliated with any private or public hospital in NYC. The number of days to obtain an appointment with each specialist was documented, along with "time on hold" when attempting to schedule an appointment and the presence of online booking capabilities.

RESULTS

Of the 1639 unique physicians affiliated with (private and public) hospitals in the 3 subspecialties, 1032 (cardiologists, endocrinologists, and ophthalmologists) were in active practice and did not require a referral. The mean wait time for scheduling an appointment was 36 (SD 36.4; IQR 12-51.5) days for cardiologists; 82 (SD 47; IQR 56-101) days for endocrinologists; and 50.4 (SD 56; IQR 10-72) days for ophthalmologists. The median wait time was 27 days for cardiologists, 72 days for endocrinologists, and 30 days for ophthalmologists. The mean time on hold while attempting to schedule an appointment with these specialists was 2.6 (SD 5.5) minutes for cardiologists, 5.4 (SD 4.3) minutes for endocrinologists, and 3.2 (SD 4.8) minutes for ophthalmologists, respectively. Over 46% (158/341) of cardiologists enabled patients to schedule an appointment on the web, and over 55% (128/228) of endocrinologists enabled patients to schedule an appointment on the web. In contrast, only approximately 25% (117/463) of ophthalmologists offered web-based appointment scheduling options.

CONCLUSIONS

The results indicate considerable variation in wait times between and within the 3 specialties examined for a new patient in NYC. Given the paucity of research on wait times for newly diagnosed people with diabetes to obtain an appointment with different specialists, this study provides preliminary estimates that can serve as an initial reference. Additional research is needed to document the extent to which wait times are associated with complications and the demographic and socio-economic characteristics of people served by different providers.

Early morning sport scheduling is associated with poorer subjective sleep characteristics in British student-athletes

This study presents the sleep characteristics of British student-athletes and examines the relationships between sport scheduling and time demands on sleep outcomes. Student-athletes (n = 157, 51% male) completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and the Sleep Hygiene Index (SHI). Self-reported sleep characteristics on weekdays and weekends, weekly frequencies of early morning and late evening sport sessions, and academic-related and sport-related time demands were also collected. Questionnaires revealed a high prevalence of undesired sleep characteristics including poor sleep quality (global PSQI >5 in 49.0%) and low sleep durations on weekdays (25% reporting <7 h). Paired t-tests revealed significant differences in bedtime, waketime, sleep duration, and sleep onset latency between weekdays and weekends (all p < 0.01). Hierarchical regression analyses indicated that early morning sport frequency was a significant predictor of PSQI (β = 0.30) and SHI (β = 0.24) global scores, weekday waketimes (β = -0.17), and weekday sleep durations (β = -0.25; all p < 0.05) in models adjusted for participant characteristics. Late evening sport frequency, and academic-related and sport-related time demands, were not significant predictors of any sleep outcome. Adjusting sport scheduling to avoid early start times could provide a means to improve sleep outcomes and may improve sporting performance and academic attainment.

The important factors nurses consider when choosing shift patterns: A cross-sectional study

AIM

To gain a deeper understanding of what is important to nurses when thinking about shift patterns and the organisation of working time.

DESIGN

A cross-sectional survey of nursing staff working across the UK and Ireland collected quantitative and qualitative responses.

METHODS

We recruited from two National Health Service Trusts and through an open call via trade union membership, online/print nursing profession magazines and social media. Worked versus preferred shift length/pattern, satisfaction and choice over shift patterns and nurses' views on aspects related to work and life (when working short, long, rotating shifts) were analysed with comparisons of proportions of agreement and crosstabulation. Qualitative responses on important factors related to shift preferences were analysed with inductive thematic analysis.

RESULTS

Eight hundred and seventy-three survey responses were collected. When nurses worked long shifts and rotating shifts, lower proportions reported being satisfied with their shifts and working their preferred shift length and pattern. Limited advantages were realised when comparing different shift types; however, respondents more frequently associated 'low travel costs' and 'better ability to do paid overtime' with long shifts and 'healthy diet/exercise' with short shifts; aspects related to rotating shifts often had the lowest proportions of agreement. In the qualitative analysis, three themes were developed: 'When I want to work', 'Impacts to my life outside work' and 'Improving my work environment'. Reasons for nurses' shift preferences were frequently related to nurses' priorities outside of work, highlighting the importance of organising schedules that support a good work-life balance.

RELEVANCE TO CLINICAL PRACTICE

General scheduling practices like adhering to existing shift work guidelines, using consistent and predictable shift patterns and facilitating flexibility over working time were identified by nurses as enablers for their preferences and priorities. These practices warrant meaningful consideration when establishing safe and efficient nurse rosters.

PATIENT OR PUBLIC CONTRIBUTION

This survey was developed and tested with a diverse group of stakeholders, including nursing staff, patients, union leads and ward managers.

REPORTING METHOD

The Strengthening the Reporting of Observational Studies (STROBE) checklist for cross-sectional studies was used to guide reporting.

Prioritising patients for semi-urgent surgery: A scoping review

BACKGROUND

Semi-urgent surgery where surgical intervention is required within 48 h of admission and the patient is medically stable is vulnerable to scheduling delays. Given the challenges in accessing health care, there is a need for a detailed understanding of the factors that impact decisions on scheduling semi-urgent surgeries.

AIM

To identify and describe the organisational, departmental and contextual factors that determine healthcare professionals' prioritising patients for semi-urgent surgeries.

METHODS

We used the Joanna Briggs Institute guidance for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) checklist. Four online databases were used: EBSCO Academic Search Complete, EBSCO Cumulative Index to Nursing and Allied Health Literature, OVID Embase and EBSCO Medline. Articles were eligible for inclusion if they published in English and focussed on the scheduling of patients for surgery were included. Data were extracted by one author and checked by another and analysed descriptively. Findings were synthesises using the Patterns, Advances, Gaps, Evidence for practice and Research recommendations framework.

RESULTS

Twelve articles published between 1999 and 2022 were included. The Patterns, Advances, Gaps, Evidence for practice and Research recommendations framework highlighted themes of emergency surgery scheduling and its impact on operating room utilisation. Gaps in the management of operating room utilisation and the incorporation of semi-urgent surgeries into operating schedules were also identified. Finally, the lack of consensus on the definition of semi-urgent surgery and the parameters used to assign surgical acuity to patients was evident.

CONCLUSIONS

This scoping review identified patterns in the scheduling methods, and involvement of key decision makers. Yet there is limited evidence about how key decision makers reach consensus on prioritising patients for semi-urgent surgery and its impact on patient experience.

PATIENT OR PUBLIC CONTRIBUTION

No Patient or Public Contribution.

An audit on surgery cancellation in a teaching hospital

Background

Operative procedure cancellations are a dilemma for the healthcare system as well as for the patients. It causes increased workload and cost to our system. For patients, it has major financial, psychological as well as medical consequences. We aim to self-identify the causes of cancellations for efficient operation room management.

Methods

We performed a retrospective chart review in a tertiary academic medical center for the last 66 months of operative records. Subsequently, we performed thematic coding to categorize causes into distinct categories.

Results

Our records showed 5153 cancellations which represent (7.3%) of the total booked procedures. Of these cancellations 91% were ordered before the day of surgery, compared to 9% for same-day cancellations. Cancellations were 58% female patients and 40% male patients. The number one reason for cancellations for both same-day and prior cancellations is the unavailability of the surgical consultant.

Conclusion

Surgical procedure cancellations profile is unique among our settings and has changed over time. Over the last 5 years, the number one reason is unavailability of the surgical consultant. Efforts should be made to identify and correct the underlying reasons to improve patient outcomes in our evolving healthcare system.

Community health pathways modeling and scheduling under uncertainty

Scheduling and coordinating constrained resources in community healthcare settings at a centralized Pathways Community HUB is challenging due to limited resources and the inherent dynamics of the processes and the organizational structures. In this work, we introduce a stochastic programming (SP) approach for connected community health for optimally scheduling community health pathways (CHPs) under uncertainty in resource availability. A CHP is a standardized tool that details multiple steps of a healthcare-related service and the required resources for each step. The SP methodology was implemented and applied to data for a real Pathways Community HUB for a U.S. county involving several CHPs, community health workers, physicians, and other resources. The computational results are promising and they show that client access times depend on the HUB resources uncertain future availability and the level of client demand, with high client demand resulting in relatively longer access time. The study reveals that schedules provided by a deterministic approach where resource availability is assumed to be known can be too optimistic. Several managerial insights are learned from this study, including the observation that the SP model provides client schedules that are equitable across the same type of community health workers.

Computational-Intelligence-Based Scheduling with Edge Computing in Cyber-Physical Production Systems

Real-time performance and reliability are two critical indicators in cyber-physical production systems (CPPS). To meet strict requirements in terms of these indicators, it is necessary to solve complex job-shop scheduling problems (JSPs) and reserve considerable redundant resources for unexpected jobs before production. However, traditional job-shop methods are difficult to apply under dynamic conditions due to the uncertain time cost of transmission and computation. Edge computing offers an efficient solution to this issue. By deploying edge servers around the equipment, smart factories can achieve localized decisions based on computational intelligence (CI) methods offloaded from the cloud. Most works on edge computing have studied task offloading and dispatching scheduling based on CI. However, few of the existing methods can be used for behavior-level control due to the corresponding requirements for ultralow latency (10 ms) and ultrahigh reliability (99.9999% in wireless transmission), especially when unexpected computing jobs arise. Therefore, this paper proposes a dynamic resource prediction scheduling (DRPS) method based on CI to achieve real-time localized behavior-level control. The proposed DRPS method primarily focuses on the schedulability of unexpected computing jobs, and its core ideas are (1) to predict job arrival times based on a backpropagation neural network and (2) to perform real-time migration in the form of human-computer interaction based on the results of resource analysis. An experimental comparison with existing schemes shows that our DRPS method improves the acceptance ratio by 25.9% compared to the earliest deadline first scheme.

A Reinforcement Learning Approach to Robust Scheduling of Permutation Flow Shop

The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum within the realm of combinatorial optimization, serving as a prevalent organizational structure in authentic production settings. Given that conventional scheduling approaches fall short of effectively addressing the intricate and ever-shifting production landscape of PFSP, this study proposes an end-to-end deep reinforcement learning methodology with the objective of minimizing the maximum completion time. To tackle PFSP, we initially model it as a Markov decision process, delineating pertinent states, actions, and reward functions. A notably innovative facet of our approach involves leveraging disjunctive graphs to represent PFSP state information. To glean the intrinsic topological data embedded within the disjunctive graph's underpinning, we architect a policy network based on a graph isomorphism network, subsequently trained through proximal policy optimization. Our devised methodology is compared with six baseline methods on randomly generated instances and the Taillard benchmark, respectively. The experimental results unequivocally underscore the superiority of our proposed approach in terms of makespan and computation time. Notably, the makespan can save up to 183.2 h in randomly generated instances and 188.4 h in the Taillard benchmark. The calculation time can be reduced by up to 18.70 s for randomly generated instances and up to 18.16 s for the Taillard benchmark.

An application of computable biomedical knowledge to transform patient centered scheduling

Introduction

Efficient appointment scheduling in the outpatient setting is challenged by two main factors: variability and uncertainty leading to undesirable wait times for patients or physician overtime, and events such as no-shows, cancellations, or walk-ins can result in physician idle time and under-utilization of resources. Some methods have been developed to optimize scheduling and minimize wait and idle times in the inpatient setting but are limited in the outpatient setting.

Methods

People and Organization Development, an internal group of organizational developers, led the development of a solution that selects the optimal group of appointments for a patient that minimizes the time between associated procedures as well as lead time built using a linear integer program. This program takes appointment requests, availability of resources, order constraints, and time preferences as inputs, and provides a list of the most optimal groupings as an output. Included in the methodology is the technical infrastructure necessary to deploy this within an electronic medical record system.

Implementation and Test Plan

A pilot has been designed to run this algorithm in a single department. The pilot will include training staff on the new workflow, and conducting informal interviews to gather qualitative data on performance. Key performance indicators such as schedule utilization, resource idle time, patient satisfaction, average appointment lead time, and average waiting time will be closely monitored.

Discussion

The model is limited in accounting for variability in appointment length potentially resulting in inaccurate schedules for healthcare providers and patients. Future states would incorporate certain visit types starting through machine learning techniques. Additionally expanding our data pipeline and processing, developing greater communication software, and expanding our research to include other departments and subspecialties, will enhance the accuracy and flexibility of the algorithm and enable healthcare providers to provide better care to their patients.

Efficient scheduling and attendance system for the ultrasound department under demand uncertainty during COVID-19

Scheduling and attendance management present huge challenges for hospitals, and the importance of both has become more critical as resource limitations and overwhelmingly uncertain demand are becoming more evident, especially during COVID-19. Important variables and factors need to be considered. When managers address this problem, they either use a manual approach or invest in expensive commercial tools. We propose a simple and flexible system that requires no extra investment. This system was developed using Ding Talk, Microsoft Excel and Visual C#. Ding Talk was used to collect vacation applications and clock information. A VBA-based Microsoft Excel program was developed to schedule shifts. A Windows Forms Application based on Visual C# was developed to complete the workload and attendance statistics. We focused on the design and implementation of the module of schedule generation and attendance management. Using the practical data of the Ultrasound Department, we compared the time spent on scheduling and attendance before and after the system was established. The results demonstrate that the system is feasible and efficient. Its high flexibility enables managers to quickly modify the schedule and attendance statistics to achieve dynamic management when dealing with inevitable demand changes during COVID-19.

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound.

Disparities in Pediatric Specialty Referral Scheduling and Completion

OBJECTIVE

To estimate differences in scheduled and completed specialty referrals by race, ethnicity, language for care, and insurance type.

STUDY DESIGN

We studied a retrospective cohort of 38 334 specialty referrals to a large children's hospital between March 2019 and March 2021. We included referrals for patients with primary care clinics within 5 miles of the hospital. We examined whether the odds of and time to scheduled and completed referrals differed by patient sociodemographic characteristics.

RESULTS

Of all referrals, 62% were scheduled and 54% were completed. Referral completion rates were lower for patients with Black race (45%), Native Hawaiian/Pacific Islander race (48%), Spanish language (49%), and public insurance (47%). Odds of scheduled and completed referral were lower for Asian (aOR scheduled: 0.94, [95% CI: 0.89, 0.99]; aOR completed: 0.92 [0.87, 0.97]), Black (aOR scheduled: 0.86 [0.79, 0.94]; aOR completed: 0.80 [0.73, 0.87]), and publicly insured patients (aOR scheduled: 0.71 [0.66, 0.75]; aOR completed: 0.70 [0.66, 0.75]). Time to scheduled and completed referral was longer for Black (adjusted hazard ratio [aHR] scheduled: 0.93 [0.88, 0.98]; aHR completed: 0.93 [0.87, 0.99]) and publicly insured patients (aHR scheduled: 0.85 [0.82, 0.88]; aHR completed: 0.84 [0.80, 0.87]) and families with a language other than English (aHR scheduled: 0.66 [0.62, 0.70]; aHR completed: 0.92 [0.86, 0.99]).

CONCLUSIONS

Within a geographically homogenous pediatric population, the odds and time to scheduled and completed specialty referrals differed by sociodemographic characteristics, suggesting the effects of discrimination. To improve access equity, health care organizations need clear and consistent referral workflows and more comprehensive metrics for access.

Human Performance of Novice Schedulers for Complex Spaceflight Operations Timelines

OBJECTIVE

Investigate the effects of scheduling task complexity on human performance for novice schedulers creating spaceflight timelines.

BACKGROUND

Future astronauts will be expected to self-schedule, yet will not be experts in creating timelines that meet the complex constraints inherent to spaceflight operations.

METHOD

Conducted a within-subjects experiment to evaluate scheduling task performance in terms of scheduling efficiency, effectiveness, workload, and situation awareness while manipulating scheduling task complexity according to the number of constraints and type of constraints.

RESULTS

Each participant (n = 15) completed a set of scheduling problems. Results showed main effects of the number of constraints and type of constraint on efficiency, effectiveness, and workload. Significant interactions were observed in situation awareness and workload for certain types of constraints. Results also suggest that a lower number of constraints may be manageable by novice schedulers when compared to scheduling activities without constraints.

CONCLUSION

Results suggest that novice schedulers' performance decreases with a high number of constraints, and future scheduling aids may need to target a specific type of constraint.

APPLICATION

Knowledge on the effect of scheduling task complexity will help design scheduling systems that will enable self-scheduling for future astronauts. It will also inform other domains that conduct complex scheduling, such as nursing and manufacturing.

Designing gene manipulation schedules for high throughput parallel construction of objective strains

Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.

Efficient Sensor Node Selection for Observability Gramian Optimization

Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.

Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks

Emerging consumer devices rely on the next generation IoT for connected support to undergo the much-needed digital transformation. The main challenge for next-generation IoT is to fulfil the requirements of robust connectivity, uniform coverage and scalability to reap the benefits of automation, integration and personalization. Next generation mobile networks, including beyond 5G and 6G technology, play an important role in delivering intelligent coordination and functionality among the consumer nodes. This paper presents a 6G-enabled scalable cell-free IoT network that guarantees uniform quality-of-service (QoS) to the proliferating wireless nodes or consumer devices. By enabling the optimal association of nodes with the APs, it offers efficient resource management. A scheduling algorithm is proposed for the cell-free model such that the interference caused by the neighbouring nodes and neighbouring APs is minimised. The mathematical formulations are obtained to carry out the performance analysis with different precoding schemes. Further, the allocation of pilots for obtaining the association with minimum interference is managed using different pilot lengths. It is observed that the proposed algorithm offers an improvement of 18.9% in achieved spectral efficiency using partial regularized zero-forcing (PRZF) precoding scheme at pilot length τp=10. In the end, the performance comparison with two other models incorporating random scheduling and no scheduling at all is carried out. As compared to random scheduling, the proposed scheduling shows improvement of 10.9% in obtained spectral efficiency by 95% of the user nodes.

A Worldwide Survey on the Practices and Perceptions of Submaximal Fitness Tests in Team Sports

PURPOSE

To survey team-sport practitioners on current practices and perceptions of submaximal fitness tests (SMFTs).

METHODS

A convenience sample of team-sport practitioners completed an online survey (September to November 2021). Descriptive statistics were used to obtain information of frequencies. A mixed-model quantile (median) regression was employed to assess the differences between the perceived influence of extraneous factors.

RESULTS

A total of 66 practitioners (74 discrete protocols) from 24 countries completed the survey. Time-efficient and nonexhaustive nature were considered the most important features of implementation. Practitioners prescribed a range of SMFTs, administered mostly on a monthly or weekly basis, but scheduling strategies appeared to differ across SMFT categories. Cardiorespiratory/metabolic outcome measures were collected in most protocols (n = 61; 82%), with the majority monitoring heart-rate-derived indices. Subjective outcome measures (n = 33; 45%) were monitored exclusively using ratings of perceived exertion. Mechanical outcome measures (n = 19; 26%) included either a combination of locomotor outputs (eg, distance covered) or variables derived from microelectrical mechanical systems. The perceived influence of extraneous factors on measurement accuracy varied according to outcome measure, and there was a lack of consensus among practitioners.

CONCLUSIONS

Our survey showcases the methodological frameworks, practices, and challenges of SMFTs in team sports. The most important features for implementation perhaps support the use of SMFTs as a feasible and sustainable tool for monitoring in team sports. The wide variety of protocols, scheduling strategies, and outcome measures, along with their associated collection and analytical techniques, may reflect the absence of robust evidence regarding the application of SMFTs in team sports.

DeSlice: An Architecture for QoE-Aware and Isolated RAN Slicing

Network slicing is considered a key feature of 5G and beyond cellular systems. It opens the door for new business models of mobile operators, enables new services, reduces costs with advanced infrastructure-sharing techniques, and improves heterogeneous traffic service. With slicing, the operators can tailor the network resources to the requirements of specific verticals, applications, and corresponding traffic types. To satisfy the heterogeneous quality of service (QoS) requirements of various slices, efficient virtualization and resource allocation algorithms are required. Such algorithms are especially crucial for the radio access network (RAN) because of the spectrum scarcity. This article develops DeSlice, a novel architecture for RAN slicing. DeSlice enables efficient real-time slicing algorithms that satisfy heterogeneous QoS requirements of the slices and improve the quality of experience for their end users. The article illustrates the advantages of DeSlice by considering the problem of the joint service of cloud VR, video, and web traffic. It develops the algorithms using DeSlice architecture and application-to-network communication. With simulations, it shows that, together, the architecture and the algorithms allow greatly improving the QoE for these traffics significantly.

Genetic Algorithm for Solving the No-Wait Three-Stage Surgery Scheduling Problem

In this research, we consider a deterministic three-stage operating room surgery scheduling problem. The three successive stages are pre-surgery, surgery, and post-surgery. The no-wait constraint is considered among the three stages. Surgeries are known in advance (elective). Multiple resources are considered throughout the surgical process: PHU (preoperative holding unit) beds in the first stage, ORs (operating rooms) in the second stage, and PACU (post-anesthesia care unit) beds in the third stage. The objective is to minimize the makespan. The makespan is defined as the maximum end time of the last activity in stage 3. Minimizing the makespan not only maximizes the utilization of ORs but also improves patient satisfaction by allowing treatments to be delivered to patients in a timely manner. We proposed a genetic algorithm (GA) for solving the operating room scheduling problem. Randomly generated problem instances were tested to evaluate the performance of the proposed GA. The computational results show that overall, the GA deviated from the lower bound (LB) by 3.25% on average, and the average computation time of the GA was 10.71 s. We conclude that the GA can efficiently find near-optimal solutions to the daily three-stage operating room surgery scheduling problem.

Alignment of RVU Targets With Operating Room Block Time

Background

Surgeon productivity is measured in relative value units (RVUs). The feasibility of attaining RVU productivity targets requires surgeons to have enough allocated block time to generate RVUs. However, it is unknown how much block time is required for surgeons to attain specific RVU targets. We aimed to estimate the effect of surgeon and practice environment characteristics (SPECs) on block time needed to attain fixed RVU targets.

Methods

We computationally simulated individual surgeons' annual caseloads under a variety of SPECs in the following way. First, empirical case data were sampled from ACS NSQIP in accordance with surgeon specialty, case-mix complexity, and RVU target. Surgeons' operating schedules were then constructed according to the block length, turnover time, and scheduling flexibility of the practice environment. These 6 SPECs were concurrently varied over their ranges for a 6-way sensitivity analysis.

Results

Annual operating schedules for 60,000,000 surgeons were simulated. The number of blocks required to attain RVU targets varied significantly with surgeon specialty and increased with increased case-mix complexity, increased turnover time, and decreased scheduling flexibility. Intraspecialty variation in block requirement with variation in environmental characteristics exceeded interspecialty variation with fixed environmental characteristics. Multivariate linear models predicted block utilization across surgical specialties with consideration for the stated factors. An online tool is shared with which to apply these results to one's particular practice.

Conclusions

Block time required to attain RVU targets varies widely with SPECs; intraspecialty variation exceeds interspecialty variation. The feasibility of attaining RVU targets requires alignment between targets and allocated operating time with consideration for surgical specialty and other practice conditions.

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights' phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles' speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow-speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.

Internet-based self-scheduling is associated with a high degree of physician satisfaction in an academic emergency medicine group

There are various methods for scheduling emergency physicians ranging from a schedule created by hand by a physician scheduler, fixed templates, to computer-assisted scheduling. The authors describe a novel method adopted by an academic emergency department using remote asynchronous physician self-scheduling. The physician group follows a pre-determined order in which each physician chooses where to place his or her assigned proportion of day/evening/weekend/night shifts on the shared calendar that is hosted in a cloud-based spreadsheet. This process gives physicians a high degree of control over their schedules, and the participants related a high degree of satisfaction regarding this process. This method of physician scheduling is a low-cost successful alternative to conventional emergency physician scheduling practices.

Real-Time Scheduling in IoT Applications: A Systematic Review

The Internet of Things (IoT) is a telecommunication network in the next generation of applications with the rapid progress of wireless sensor network techniques that have touched many spheres of life today. Hardware, telephony, communications, storage, secure platforms, software and services, and data processing platforms are all part of the IoT environment. IoT sensors collect data from their environment and share it by connecting to the Internet gateway. These sensors often perform tasks without human intervention. This article aims to review real-time scheduling in the IoT to fully understand the issues raised in this area published from 2018 to 2022. A classification for IoT applications based on practical application is provided for selected studies. Selected studies include healthcare, infrastructure, industrial applications, smart city, commercial applications, environmental protection, and general IoT applications. Studies are sorted into groups based on related applications and compared based on indicators such as performance time, energy consumption, makespan, and assessment environments depending on the provided classification. Finally, this paper discusses all reviewed studies' main concepts, disadvantages, advantages, and future work.

Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications

In this paper, we investigate the problem of reducing the use of radio resources for vehicle-to-vehicle communications in an autonomous platooning scenario. Achieving reliable communications, which is a key element allowing for the tight coordination of platoon vehicles' motion, might be challenging in a case of heavy road traffic. Thus, in this paper, we propose to reduce the number of intra-platoon transmissions required to facilitate the safe autonomous control of vehicle mobility, by analyzing the impact of cars' behaviors (in terms of acceleration changes) on the evolution of the inter-vehicle distance errors within the platoon. We derive formulas representing the relation between the platoon leader's acceleration changes and the evolution of the distance error, velocity difference, and the accelerations for the first pair of vehicles. Furthermore, we propose a heuristic algorithm for selection of the intra-platoon messaging period for each platoon vehicle that minimizes the use of radio resources subject to the safety constraint, represented as the fraction of the total time when emergency braking is activated. The presented simulation results indicate that the proposed approach is capable of ensuring safe platoon operation and simultaneously providing a significant reduction in the use of resources, compared with conventional fixed-period transmission.

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

Energy-Efficient Uplink Scheduling in Narrowband IoT

This paper presents a detailed study of uplink scheduling in narrowband internet of things (NB-IoT) networks. As NB-IoT devices need a long battery lifetime, we aim to maximize energy efficiency while satisfying the main requirements for NB-IoT devices. Also, as the NB-IoT scheduling problem is divided into link adaptation problem and resource allocation problem, this paper investigates the correlation between these two problems. Accordingly, we propose two scheduling schemes: the joint scheduling scheme, where the two problems are combined as one optimization problem, and the successive scheduling scheme that manages each problem separately but successively. Each scheme aims to maximize energy efficiency while achieving reliable transmission, satisfying delay requirements, and guaranteeing resource allocation specifications. Also, we investigate the impact of the selected devices to be served on the total energy efficiency. Accordingly, we propose two device selection techniques to maximize the total energy efficiency. The first technique exhaustively searches for the optimal devices, while the second sorts the devices based on a proposed priority score. The simulation results compare the successive and the joint scheduling schemes. The results show that the joint scheme outperforms the successive scheme in terms of energy efficiency and the number of served devices but with higher complexity. Also, the results highlight the impact of each proposed selection technique on the scheduling schemes' performance.

The Differential Impact of COVID-19 on Labour Market Outcomes of Immigrants in Canada

Based on Canadian Labour Force Survey data, we estimate the differential effect of the COVID-19 pandemic on seven labour market outcomes, and separate between recent and established immigrants relative to domestic-born Canadians. We also use Recentered Influence Function (RIF) unconditional quantile regressions to estimate the differential effects across the distribution of outcomes. We find that the pandemic had an adverse effect on the labour market outcomes for all workers, and that the adverse effects were generally larger for immigrants and especially recent immigrants as well as for immigrants at the bottom of the outcome distributions. The adverse effects were generally larger at the earliest waves of the pandemic, and for recent immigrants who were female, less educated, and those with child responsibilities, and for jobs at greater risk of contact with the pandemic.

Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations

The 6G wireless systems are expected to have higher capacity, reliability, and energy efficiency than the existing cellular systems. Millimeter-wave (mmWave) frequencies offer high capacity at the cost of high attenuation and blockage losses. Reconfigurable intelligent surface (RIS) assisted mmWave networks consist of smaller antenna elements that control the propagation channel between the base station (BS) and the user by appropriately tuning the phase and the reflection of the incident electromagnetic signal. The deployment of RIS is considered to be an energy efficient solution to improve the coverage of regions with high blocking probability. However, if every BS is associated with one or more dedicated RIS, then the density of RIS increases proportionally with the density of BSs. Hence in this work, we propose RIS sharing mechanisms where multiple BSs share one RIS. We formulate resource allocation of RIS sharing in terms of time and the RIS elements as an optimization problem, and we propose heuristics to solve both. Further, we present detailed simulation results to compare time and the element based RIS sharing methods for various scenarios with the benchmark and the RIS system without sharing. The proposed time and element based RIS sharing methods improve throughput upto 53% and 25%, respectively, compared to the RIS system without sharing in specific scenarios.

A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems

For the receiver of multiple-input multiple-output (MIMO) systems, linear detectors are an interesting option due to their good performance and low complexity. Nevertheless, MIMO systems lose diversity in exchange for eliminating interference when linear detectors are used. Aiming to maintain the system diversity while mitigating interference between users, this work proposes a scheduling scheme for the uplink of multiuser MIMO (MU-MIMO) systems that employ A antennas and the zero-forcing (ZF) detector at the receiver in the base station (BS). The channel model includes Rician fading and additive white Gaussian noise (AWGN) in an imperfect channel estimation scenario. The proposed scheme selects U users from a group of Ut users to transmit simultaneously, so that the signal-to-noise ratio (SNR) is maximized. For this, an exact expression to evaluate the SNR of the users is obtained. With this result, the scheduling strategy is proposed. Results show that as Ut increases, the outage probability (OP), and the bit error rate (BER) decrease as the system diversity increases, even when the system is completely loaded, i.e., when U=A. Moreover, it is shown that the scheduling scheme counteracts the imperfect channel estimation effects as Ut increases. Finally, the proposed scheme is tested in presence of an external eavesdropper trying to decode the information sent by the users. The results show that the presented proposal allows for a reduction of the secrecy-outage-probability (SOP) as Ut increases.

Automated patient self-scheduling: case study

This case study assesses the uptake, user characteristics, and outcomes of automated self-scheduling in a community-based physician group affiliated with an academic health system. We analyzed 1 995 909 appointments booked between January 1, 2019, and June 30, 2021 at more than 30 practice sites. Over the study period, uptake of self-scheduling increased from 4% to 15% of kept appointments. Younger, commercially insured patients were more likely to be users. Missed appointments were lower and cancelations were higher for self-scheduled patients. An examination of characteristics, benefits, and usage of automated self-scheduling provides insight to those organizations contemplating the implementation or expansion of similar consumer-facing digital self-scheduling platforms.

MAC Protocols for mmWave Communication: A Comparative Survey

With the increase in the number of connected devices, to facilitate more users with high-speed transfer rate and enormous bandwidth, millimeter-wave (mmWave) technology has become one of the promising research sectors in both industry and academia. Owing to the advancements in 5G communication, traditional physical (PHY) layer-based solutions are becoming obsolete. Resource allocation, interference management, anti-blockage, and deafness are crucial problems needing resolution for designing modern mmWave communication network architectures. Consequently, comparatively new approaches such as medium access control (MAC) protocol-based utilization can help meet the advancement requirements. A MAC layer accesses channels and prepares the data frames for transmission to all connected devices, which is even more significant in very high frequency bands, i.e., in the mmWave spectrum. Moreover, different MAC protocols have their unique limitations and characteristics. In this survey, to deal with the above challenges and address the limitations revolving around the MAC layers of mmWave communication systems, we investigated the existing state-of-the-art MAC protocols, related surveys, and solutions available for mmWave frequency. Moreover, we performed a categorized qualitative comparison of the state-of-the-art protocols and finally examined the probable approaches to alleviate the critical challenges in future research.

Application of Blockchain Technology in Production Scheduling and Management of Human Resources Competencies

Today, enterprises are multitasking, with branches set up all over the world. Virtual enterprises are created to make better use of existing resources, improve the quality of manufactured products and agilely respond to customer requirements. In order to fully meet the requirements of enterprises, a decentralized structure of data registration and transmission and authentication of network users is needed. The information collected via the Internet of Things and flowing based on the properties of the Blockchain (BC) network facilitates enterprise resource planning and enables the integration of internal processes, especially when planning, changing the current or introducing new production. The aim of this paper is to present the concept of using a common data register in BC technology, which enables a number of applications related to the automation of the process of selecting human resources for production tasks. The paper presents an analysis of the problem related to the integration of production scheduling and human resource management with blockchain technology. Also presented is a literature analysis on scheduling, blockchain technology and data storage in the blockchain network. The analysis presents how the blockchain network works and how exactly it fits into production engineering with its advantages and disadvantages. An employee evaluation method based on the resource work history and determination of its current value within individual competencies is presented. The integration of production scheduling and human resource management with the use of BC technology is simulated. The most important advantage is faster and more effective planning thanks to the elimination of all intermediary channels in the flow of production transactions. Production tasks are balanced with production capacity in entities belonging to the virtual enterprise in parallel. For future research, different online planning algorithms will be developed and compared to achieve consortium members’ consensus on production and human resources planning.

Nurse workforce scheduling: A qualitative study of Indonesian nurse managers' experiences during the COVID-19 pandemic

Background

The increase in COVID-19 cases in Indonesia has resulted in changes in the hospital workflow, including the staffing process and scheduling, especially in the isolation units. Nurse managers are working hard in the scheduling system to ensure high-quality care is provided with the best human resources.

Objective

This study aimed to explore the experiences of nurse managers in managing staff nurses' work schedules during the COVID-19 pandemic.

Methods

A qualitative descriptive design was used in this study. Eleven nurse managers from three COVID-19 referral hospitals were selected using purposive sampling. Data were collected using online semi-structured interviews. Thematic analysis was used for data analysis, and data were presented using a thematic tree. Consolidated criteria for reporting qualitative research (COREQ) checklist was used as a reporting guideline of the study.

Results

Four themes were developed: (i) Nurse shortage, (ii) Strategically looking for ways to fulfill the workforce, (iii) Change of shift schedule, and (iv) Expecting guidance from superiors and compliance from staff.

Conclusion

The lack of nurse staff is a problem during a pandemic. Thus, managing personnel effectively, mobilizing and rotating, and recruiting volunteers are strategies to fulfill the workforce during the pandemic. Using a sedentary shift pattern and sufficient holidays could prevent nurses from falling ill and increase compliance with scheduling. In addition, a staffing calculation formula is needed, and top nursing managers are suggested to provide guidance or direction to the head nurses to reduce confusion in managing the work schedule during the pandemic.

Impact of Microcycle Structures on Physical and Technical Outcomes During Professional Rugby League Training and Matches

PURPOSE

To assess the impact of microcycle (MC) structures on physical and technical performances in rugby league training and matches.

METHODS

Thirty-four professional rugby league players were monitored during all training sessions and matches across a single season wherein 2 different competition-phase MC structures were implemented. The first MC structure involved the first session on match day (MD) + 2 and the main stimulus delivered MD - 3, and the second structure delayed all sessions by 1 day (first session on MD + 3 and main session MD - 2; MC structure in the second half of the season). Physical output was quantified via relative total speed (in meters per minute), high-speed running (per minute; ≥4.0 m·s-1), and very-high-speed running (per minute; ≥5.5 m·s-1), measured using a global positioning system (10 Hz) in addition to accelerometer (100 Hz) metrics (PlayerLoad per minute and PlayerLoadslow per minute]) during training and matches. Technical performance (number of runs, meters gained, tackles made and missed) was recorded during matches. Generalized linear mixed models and equivalence tests were used to identify the impact of MC structure on physical and technical output.

RESULTS

Nonequivalent increases in meters per minute, high-speed running per minute, and PlayerLoad per minute were observed for the first training stimulus in MC structure in the second half of the season with no practical difference in midcycle sessions observed. The MC structure in the second half of the season structure resulted in increased high-speed running per minute and decreased PlayerLoadslow per minute during MD with no differences observed in technical performance.

CONCLUSIONS

Delaying the first training stimulus of the MC allowed for greater training load accumulation without negative consequences in selected match running and technical performance measures. This increased MC load may support the maintenance of physical capacities across the in-season.

Towards LoRaWAN without Data Loss: Studying the Performance of Different Channel Access Approaches

The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN.

Vaccination Schedule under Conditions of Limited Vaccine Production Rate

The paper is devoted to optimal vaccination scheduling during a pandemic to minimize the probability of infection. The recent COVID-19 pandemic showed that the international community is not properly prepared to manage a crisis of this scale. Just after the vaccines had been approved by medical agencies, the policymakers needed to decide on the distribution strategy. To successfully fight the pandemic, the key is to find the equilibrium between the vaccine distribution schedule and the available supplies caused by limited production capacity. This is why society needs to be divided into stratified groups whose access to vaccines is prioritized. Herein, we present the problem of distributing protective actions (i.e., vaccines) and formulate two mixed-integer programs to solve it. The problem of distributing protective actions (PDPA) aims at finding an optimal schedule for a given set of social groups with a constant probability of infection. The problem of distributing protective actions with a herd immunity threshold (PDPAHIT) also includes a variable probability of infection, i.e., the situation when herd immunity is obtained. The results of computational experiments are reported and the potential of the models is illustrated with examples.

Barriers to and Facilitators of Automated Patient Self-scheduling for Health Care Organizations: Scoping Review

Background

Appointment management in the outpatient setting is important for health care organizations, as waits and delays lead to poor outcomes. Automated patient self-scheduling of outpatient appointments has demonstrable advantages in the form of patients’ arrival rates, labor savings, patient satisfaction, and more. Despite evidence of the potential benefits of self-scheduling, the organizational uptake of self-scheduling in health care has been limited.

Objective

The objective of this scoping review is to identify and to catalog existing evidence of the barriers to and facilitators of self-scheduling for health care organizations.

Methods

A scoping review was conducted by searching 4 databases (PubMed, CINAHL, Business Source Ultimate, and Scopus) and systematically reviewing peer-reviewed studies. The Consolidated Framework for Implementation Research was used to catalog the studies.

Results

In total, 30 full-text articles were included in this review. The results demonstrated that self-scheduling initiatives have increased over time, indicating the broadening appeal of self-scheduling. The body of literature regarding intervention characteristics is appreciable. Outer setting factors, including national policy, competition, and the response to patients’ needs and technology access, have played an increasing role in influencing implementation over time. Self-scheduling, compared with using the telephone to schedule an appointment, was most often cited as a relative advantage. Scholarly pursuit lacked recommendations related to the framework’s inner setting, characteristics of individuals, and processes as determinants of implementation. Future discoveries regarding these Consolidated Framework for Implementation Research domains may help detect, categorize, and appreciate organizational-level barriers to and facilitators of self-scheduling to advance knowledge regarding this solution.

Conclusions

This scoping review cataloged evidence of the existence, advantages, and intervention characteristics of patient self-scheduling. Automated self-scheduling may offer a solution to health care organizations striving to positively affect access. Gaps in knowledge regarding the uptake of self-scheduling by health care organizations were identified to inform future research.

Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%.

A Survey of 802.15.4 TSCH Schedulers for a Standardized Industrial Internet of Things

Concepts such as Industry 4.0 and Cyber-Physical Systems may bring forward a new industrial revolution. These concepts require extensive connectivity far beyond what is provided by traditional industrial networks. The Industrial Internet of Things (IIoT) bridges this gap by employing wireless connectivity and IP networking. In order for wireless networks to meet the strict requirements of the industrial domain, the Time Slotted Channel Hopping (TSCH) MAC is often employed. The properties of a TSCH network are defined by the schedule, which dictates transmission opportunities for all nodes. We survey the literature for these schedulers, describe and organize them according to their operation: Centralized, Collaborative, Autonomous, Hybrid, and Static. For each category and the field as a whole, we provide a holistic view and describe historical trends, highlight key developments, and identify trends, such as the attention towards autonomous mechanisms. Each of the 76 schedulers is analyzed into their common components to allow for comparison between schedulers and a deeper understanding of functionality and key properties. This reveals trends such as increasing complexity and the utilization of centralized principles in several collaborative schedulers. Further, each scheduler is evaluated qualitatively to identify its objectives. Altogether this allows us to point out challenges in existing work and identify areas for future research, including fault tolerance, scalability, non-convergecast traffic patterns, and hybrid scheduling strategies.

Digital Twin-Driven Human Robot Collaboration Using a Digital Human

Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems.

Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints

In automated laboratories consisting of multiple different types of instruments, scheduling algorithms are useful for determining the optimal allocations of instruments to minimize the time required to complete experimental procedures. However, previous studies on scheduling algorithms for laboratory automation have not emphasized the time constraints by mutual boundaries (TCMBs) among operations, which is important in procedures involving live cells or unstable biomolecules. Here, we define the "scheduling for laboratory automation in biology" (S-LAB) problem as a scheduling problem for automated laboratories in which operations with TCMBs are performed by multiple different instruments. We formulate an S-LAB problem as a mixed-integer programming (MIP) problem and propose a scheduling method using the branch-and-bound algorithm. Simulations show that our method can find the optimal schedules of S-LAB problems that minimize overall execution time while satisfying the TCMBs. Furthermore, we propose the use of our scheduling method for the simulation-based design of job definitions and laboratory configurations.

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.

Redesigning the Process for Scheduling Elective Orthopaedic Surgery: A Combined Lean Six Sigma and Person-Centred Approach

The Health Service Executive Ireland model of care for elective surgery supports the delivery of elective surgical care in achieving both process and clinical outcomes. This project was conducted in the Orthopaedic Department. Following an outpatient consultation with an orthopaedic surgeon, patients who required surgical intervention were scheduled for their intervention by the administrative team. Prior to commencing this project, the average time from patient consultation to being scheduled for surgery on the hospital system was 62 h/2.58 days. A pre- and post-team-based intervention design employing Lean Six Sigma methodology was applied to redesign the process for scheduling elective orthopaedic surgery. The project was informed by collaborative, inclusive, and participatory stakeholder engagement. The goal was to streamline the scheduling process for elective orthopaedic surgery, with a target that 90% of surgeries are scheduled “right first time” within 48 h/two working days of the outpatient consultant appointment. The main outcome measures showed that 100% of orthopaedic surgeries were scheduled successfully within 2 days of outpatient appointment. Duplication in work between patient services and scheduling teams was eliminated and facilitated a reduction in unnecessary staff workload. This project highlights the importance of collaborative interdisciplinary stakeholder engagement in the redesigning of processes to achieve sustainable outcomes, and the findings have informed further improvements across the hospital’s surgical scheduling system.

Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing

Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows with data and compute-intensive tasks and also have some special characteristics. These characteristics include the tasks of scientific workflows that are executed in terms of integration, disintegration, pipeline, and parallelism, and thus require special attention to task management and data-oriented resource scheduling and management. The tasks executed during pipeline are considered as bottleneck executions, the failure of which result in the wholly futile execution, which requires a fault-tolerant-aware execution. The tasks executed during parallelism require similar instances of cloud resources, and thus, cluster-based execution may upgrade the system performance in terms of make-span and execution cost. Therefore, this research work presents a cluster-based, fault-tolerant and data-intensive (CFD) scheduling for scientific applications in cloud environments. The CFD strategy addresses the data intensiveness of tasks of scientific workflows with cluster-based, fault-tolerant mechanisms. The Montage scientific workflow is considered as a simulation and the results of the CFD strategy were compared with three well-known heuristic scheduling policies: (a) MCT, (b) Max-min, and (c) Min-min. The simulation results showed that the CFD strategy reduced the make-span by 14.28%, 20.37%, and 11.77%, respectively, as compared with the existing three policies. Similarly, the CFD reduces the execution cost by 1.27%, 5.3%, and 2.21%, respectively, as compared with the existing three policies. In case of the CFD strategy, the SLA is not violated with regard to time and cost constraints, whereas it is violated by the existing policies numerous times.

An Energy-Friendly Scheduler for Edge Computing Systems

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.

Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring

In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.

Minimum Length Scheduling for Multi-Cell Full Duplex Wireless Powered Communication Networks

Wireless powered communication networks (WPCNs) will be a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. These MTC networks will be deployed by using low-power transceivers and a very limited set of transmission configurations. We investigate a novel minimum length scheduling problem for multi-cell full-duplex wireless powered communication networks to determine the optimal power control and scheduling for constant rate transmission model. The formulated optimization problem is combinatorial in nature and, thus, difficult to solve for the global optimum. As a solution strategy, first, we decompose the problem into the power control problem (PCP) and scheduling problem. For the PCP, we propose the optimal polynomial time algorithm based on the evaluation of Perron-Frobenius conditions. For the scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. Through extensive simulations, we demonstrate a 50% reduction in the schedule length by using the proposed algorithm in comparison to unscheduled concurrent transmissions.

Working Conditions Influencing Drivers' Safety and Well-Being in the Transportation Industry: "On Board" Program

The conditions of work for professional drivers can contribute to adverse health and well-being outcomes. Fatigue can result from irregular shift scheduling, stress may arise due to the intense job demands, back pain may be due to prolonged sitting and exposure to vibration, and a poor diet can be attributed to limited time for breaks and rest. This study aimed to identify working conditions and health outcomes in a bussing company by conducting focus groups and key informant interviews to inform a Total Worker Health^® organizational intervention. Our thematic analysis identified three primary themes: lack of trust between drivers and supervisors, the scheduling of shifts and routes, and difficulty performing positive health behaviors. These findings demonstrate the value of using participatory methods with key stakeholders to determine the unique working conditions and pathways that may be most critical to impacting safety, health, and well-being in an organization.

IEEE 802.11ax OFDMA Resource Allocation with Frequency-Selective Fading

This paper studies the usage of orthogonal frequency division multiple access (OFDMA) for uplink transmissions in IEEE 802.11ax networks. OFDMA enables simultaneous multi-user transmissions in Wi-Fi, but its usage requires efficient resource allocation algorithms. These algorithms should be able to adapt to the changing channel conditions, including the frequency-selective fading. This paper presents an OFDMA resource allocation algorithm for channels with frequency-selective fading and proposes an approach to adapt the user transmission power and modulation and coding schemes to the varying channel conditions, which is efficient even in the case when the access point has outdated channel state information. The proposed scheduling algorithm and power allocation approach can double the goodput and halve the data transmission time in Wi-Fi networks even in dense deployments of access points.